We present a set of phenomena that can be used for evaluating
cognitive architectures that aim at being designs for intelligent systems. To date, we know of few architectures that address more than a handful of these phenomena, and none that are able to explain all of them. Thus, these phenomena test the generality of a system and can be used to point out weaknesses in an architecture's design. The phenomena encourage autonomous learning, development of representations, and domain independence, which we argue are critical for a solution to the AI problem.

Subjects: 2. Architectures;
12. Machine Learning and Discovery

Submitted: May 15, 2007

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